Výsledky vyhľadávania - "IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning"

2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)

2013 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)

2011 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)

2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning

Advanced Machine Learning Model Based on Bi-LSTM and Attention Mechanism for Fault Detection in Wind Turbine Systems.

Autori: Pham, Duc-Anh Han, Seung-Hun

Zdroj: Journal of Electrical Engineering & Technology (19750102); Nov2025, Vol. 20 Issue 8, p5429-5442, 14p

Segmenting Action-Value Functions over Time Scales in SARSA via TD(Δ).

Autori: Humayoo, Mahammad Zheng, Gengzhong Dong, Xiaoqing a ďalší

Zdroj: Algorithms; Nov2025, Vol. 18 Issue 11, p729, 31p

Predmety: REINFORCEMENT learning

Efficient Parameter Search for Chaotic Dynamical Systems Using Lyapunov-Based Reinforcement Learning.

Autori: Huang, Gang-Cheng

Zdroj: Symmetry (20738994); Nov2025, Vol. 17 Issue 11, p1832, 20p

Predmety: REINFORCEMENT learning, LYAPUNOV exponents, CHAOS theory, ADAPTIVE control systems, ENGINEERING design, MATHEMATICAL optimization

User comfort or sustainability? A multi-objective reinforcement learning strategy for the IoMT with harvesting energy.

Autori: Mzid, Rania Haouari, Bakhta Mosbahi, Olfa

Zdroj: Cluster Computing; Oct2025, Vol. 28 Issue 9, p1-21, 21p

Predmety: INTERNET of medical things, REINFORCEMENT learning, BODY area networks, SUSTAINABILITY, USER experience, ENERGY harvesting, MARKOV processes

Exploring reinforcement learning in process control: a comprehensive survey.

Autori: Rajasekhar, N. Radhakrishnan, T.K. Samsudeen, N.

Zdroj: International Journal of Systems Science; Oct2025, Vol. 56 Issue 14, p3528-3557, 30p

Predmety: REINFORCEMENT learning, PROCESS control systems, MACHINE learning, DECISION making, ALGORITHMS, DEEP learning, INDUSTRIAL applications

Multi‐Objective Reinforcement Learning for Automated Resilient Cyber Defence.

Autori: O'Driscoll, Ross Hagen, Claudia Bater, Joe a ďalší

Zdroj: Applied AI Letters; Oct2025, Vol. 6 Issue 3, p1-15, 15p

Predmety: REINFORCEMENT learning, COMPUTER network security, CYBERTERRORISM, INTELLIGENT agents, INTERNET security

Reinforcement learning control with function approximation via multivariate simplex splines.

Autori: Feng, Yiting Zhou, Ye Ho, Hann Woei a ďalší

Zdroj: International Journal of Adaptive Control & Signal Processing; Oct2025, Vol. 39 Issue 10, p2040-2061, 22p

Predmety: REINFORCEMENT learning, SPLINES, NONLINEAR systems, INVERTED pendulum (Control theory), OPTIMAL control theory, MATHEMATICAL analysis

An adaptive linear quadratic tracker design for continuous – time systems with completely unknown dynamics.

Autori: Mishra, Hari Om Shanker Jha, Sumit Kumar Dhawan, Amit a ďalší

Zdroj: International Journal of Systems Science; Jan2026, Vol. 57 Issue 2, p311-332, 22p

Enhancing Energy Generation While Mitigating Noise Emissions in Wind Turbines Through Multi‐Objective Optimization: A Deep Reinforcement Learning Approach.

Autori: Frutos, Martín Marino, Oscar A. Huergo, David a ďalší

Zdroj: Wind Energy; Aug2025, Vol. 28 Issue 8, p1-19, 19p

Predmety: WIND turbines, REINFORCEMENT learning, CLEAN energy, TORQUE control, ENERGY development, MULTI-objective optimization, NOISE pollution, ACOUSTIC models

Anticipatory scheduling of synchromodal transport using approximate dynamic programming.

Autori: Rivera, Arturo E. Pérez Mes, Martijn R. K.

Zdroj: Annals of Operations Research; Jul2025, Vol. 350 Issue 1, p95-129, 35p

Predmety: DYNAMIC programming, SCHEDULING, DECISION making, CONTAINERIZATION, MARKOV processes, STOCHASTIC programming, HEURISTIC

hammer: Multi-level coordination of reinforcement learning agents via learned messaging.

Autori: Gupta, Nikunj Srinivasaraghavan, G. Mohalik, Swarup a ďalší

Zdroj: Neural Computing & Applications; Jul2025, Vol. 37 Issue 19, p13221-13236, 16p

Predmety: ARTIFICIAL neural networks, ARTIFICIAL intelligence, REINFORCEMENT learning, IMAGE processing, MARL

Optimized Look-Ahead Trees: Extensions to Large and Continuous Action Spaces

Autori: Jung, Tobias Ernst, Damien Maes, Francis

Zdroj: Proc. of IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL'13) (2013); IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL'13), from 16-04-2013 to 19-04-2013

Predmety: Look-ahead trees, Planning, Optimal Control, Engineering, computing & technology, Computer science, Ingénierie, informatique & technologie, Sciences informatiques

Relation: https://orbi.uliege.be/handle/2268/142357; info:hdl:2268/142357; https://orbi.uliege.be/bitstream/2268/142357/1/adprl13_web.pdf

Dostupnosť: https://orbi.uliege.be/handle/2268/142357
https://orbi.uliege.be/bitstream/2268/142357/1/adprl13_web.pdf

On Neural Consolidation for Transfer in Reinforcement Learning

Autori: Guillet, Valentin Wilson, Dennis G. Aguilar-Melchor, Carlos a ďalší

Témy: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, text

URL: http://arxiv.org/abs/2210.02240

A review on reinforcement learning algorithms and applications in supply chain management.

Autori: Rolf, Benjamin Jackson, Ilya Müller, Marcel a ďalší

Zdroj: International Journal of Production Research; Oct2023, Vol. 61 Issue 20, p7151-7179, 29p, 3 Color Photographs, 1 Diagram, 6 Charts, 3 Graphs

Predmety: MACHINE learning, SUPPLY chain management, INVENTORY control, REINFORCEMENT learning, SUPPLY chains

From single-objective to multi-objective reinforcement learning-based model transformation.

Autori: Eisenberg, Martin Wimmer, Manuel

Zdroj: Software & Systems Modeling; Jun2025, Vol. 24 Issue 3, p891-921, 31p

Predmety: MACHINE learning, MODELING languages (Computer science), COMPUTATIONAL mathematics, GENETIC algorithms, REINFORCEMENT learning, METAHEURISTIC algorithms

Navigation and Obstacle Avoidance for USV in Autonomous Buoy Inspection: A Deep Reinforcement Learning Approach.

Autori: Wang, Jianhui Lu, Zhiqiang Hong, Xunjie a ďalší

Zdroj: Journal of Marine Science & Engineering; May2025, Vol. 13 Issue 5, p843, 23p

Predmety: DEEP reinforcement learning, REINFORCEMENT learning, COLLISIONS at sea, AUTONOMOUS vehicles, NAVIGATION